Artificial Intelligence in Radiology, Computer-Aided Detection, Early-Stage Lung Cancer, Lung Cancers, Lung Nodules
Conditions
Keywords
Lung Nodules, Lung Cancer, Early-Stage Lung Cancer, Chest X-Ray (CXR), Artificial Intelligence (AI), Computer-Aided Detection (CADe), Cancer Screening, FDA-Cleared Device
Brief summary
The study evaluates the impact of qXR-LN compared to standard radiologist-only interpretations before and after AI deployment. The goal is to compare how well lung nodules and cancers are detected in two time periods: before and after the implementation of the AI tool in routine clinical practice. The study aims to determine whether the AI system can help radiologists identify more actionable lung nodules and diagnose lung cancer earlier, ultimately improving patient outcomes. No changes will be made to patients' standard care, and all treatment decisions will be based on the clinical judgment of physicians. The study includes patients over 35 years old who undergo chest X-rays for various medical reasons, excluding those with known lung cancer.
Detailed description
This study evaluates the clinical impact of the FDA-cleared artificial intelligence (AI) tool, qXR-LN, for detecting lung nodules and diagnosing lung cancer using chest X-rays (CXR). The study employs an ambispective observational cohort design with two cohorts: pre-deployment (before AI implementation) and post-deployment (after AI implementation). The primary objective is to assess differences in lung nodule detection rates and the percentage of lung cancers diagnosed through the nodule pathway between the two cohorts. Secondary objectives include evaluating whether the AI tool aids in detecting more early-stage lung cancers and identifying reasons for patients dropping out of the nodule clinic pathway. In the post-deployment cohort, qXR-LN integrates seamlessly with the hospital's existing systems to provide real-time AI findings on radiologists' workstations. Radiologists can accept or reject AI suggestions, ensuring that the final decisions remain under human supervision. Data from both cohorts, including patient demographics, nodule detection rates, cancer diagnoses, and treatment outcomes, will be collected and analyzed. The study excludes patients under 35 years old and those with known lung cancer at the time of imaging. Ethical considerations include obtaining waivers of consent where applicable and ensuring minimal risk to participants. The findings of this study aim to inform clinical practices and enhance the use of AI tools in lung cancer screening and diagnosis.
Interventions
None listed
Sponsors
University of Florida
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
35 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age ≥35 years at the time of chest X-ray acquisition * Chest X-ray must be obtained as part of routine care (e.g., ordered for respiratory complaints, screening, or other clinical indications) * Chest X-ray performed using CR/DR/DX imaging modality * Examination described as Chest * View: PA or AP * Patient positioned as Erect or Supine * Image available in valid DICOM format with proper DICOM prefix values (including DICM in the header)
Exclusion criteria
* Patients aged \<35 years at the time of chest X-ray * Patients with known lung cancer at the time of chest X-ray acquisition * Lateral views or any imaging modality other than CR/DR/DX * Imaging or anatomy not specified as Chest (e.g., different body parts or modalities)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Difference in Nodule Detection Rate Between Pre- and Post-Deployment Cohorts | Through study completion, approximately 12 months. | Compare the proportion of patients with lung nodules detected on chest X-rays before and after implementing the AI tool (qXR-LN). Lung nodule detection will be determined by radiological interpretation of chest X-rays. For the pre-deployment cohort, the presence or absence of nodules will be derived from radiology reports and confirmed by a clinical research associate. For the post-deployment cohort, nodules identified by qXR-LN and subsequently reviewed by radiologists (using the qTrack tool) will serve as the primary measure. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Percentage of Lung Cancer Diagnosed Through Nodule Pathway | Through study completion, approximately 12 months. | Assess the difference in the percentage of lung cancer cases diagnosed via the nodule pathway in pre- and post-deployment cohorts. Lung cancer diagnosis and staging will be confirmed through pathology reports (biopsy results) and/or imaging follow-up (CT/PET), as documented in the electronic health record (EHR) and the Radiology Information System (RIS). |
| Detection of Early-Stage Lung Cancer | Through study completion, approximately 12 months. | Compare the proportion of early-stage (Stage I and Stage II) lung cancer diagnoses between pre- and post-deployment cohorts. Staging will be obtained from the pathology report, imaging studies (CT scans), and clinical documentation in the EHR. Established TNM (Tumor, Node, Metastasis) classification guidelines will be used for determining cancer stage. |
| Reasons for Dropout from Nodule Clinic Pathway | Through study completion, approximately 12 months. | Summarize and analyze reasons for patients not completing the nodule clinic pathway in the post-deployment cohort. Reasons for dropout will be extracted from patient records, clinical notes, follow-up logs, and administrative records. These data may include documentation of patient contact attempts, scheduling records, and physician or nurse notes indicating patient-reported reasons for not completing the pathway. |
Outcome results
None listed